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Advanced non-small-cell lung cancer, as well as pancreatic and breast cancer.

Advanced non-small-cell lung cancer, as well as pancreatic and breast cancer. However, current therapy treats only a subset of patients carrying specific mutations and even within this population, tumor resistance is common. Identification of specific protein targets involved in ErbB-mediated cancer development is confounded by the multiplicity of pathways activated by ErbB receptors and the existence of more than 100 potential protein binding 2-(Phosphonomethyl)pentanedioic acid web partners identified by large-scale phosphoproteomic screening. As members of the ErbB receptor family cooperate in signal transduction and malignant transformation, the concurrent inhibition of two or more receptors or specific heteromeric ErbB family receptor complexes may yield the next generation targeted therapies. However, only a small proportion of publicly available bioactivity data reports on the activation of ErbB oligomers. In many cases, the exact mechanism of ligand-protein binding and protein activation is simply not known and bioactivity of small molecules is tested on single proteins only. This leads to challenges for structure-based drug design and interpretation of pharmacological data. As such, understanding the role of receptor oligomers in the ErbB signaling pathway is invaluable for the purpose of drug discovery. Pathway targets and pharmacology In total, 54 NCBI Gene IDs were retrieved as targets from the ErbB signaling pathway. Of those, only 35 single proteins returned pharmacological data with the applied bioactivity filters. Additionally, data for 12 protein families, 5 protein complexes, 2 protein-protein interactions and one chimeric protein containing a target from the pathway were retrieved, increasing the total number of targets to 55. While a pharmacology query without any filters would retrieve nearly 150,000 data points, filtering reduced the data to 108,014 bioactivities and 65,780 unique compounds. Using the pChEMBL values to filter bioactivities led to a significantly lower number of records as compared to -logActivity values: 53 targets, 65,817 bioactivity endpoints and 43,255 unique compounds. The pChEMBL filter restricts data to those that are equal to a specific value. Values that are reported to be `greater than’ or `less than’ will therefore be missing in the 13 / 32 Open PHACTS and Drug Discovery Research Fig.3. Use case B workflow. Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Compound pharmacology at the pathway level was retrieved by consecutive execution of the API calls `Pathway Information: Get targets’ PubMed ID:http://jpet.aspetjournals.org/content/119/3/418 and `Target Pharmacology: List’ – the latter includes a filtering for desired activity endpoints and units – and other filtering, transformation, and normalization steps: transformation into `- logActivity values ‘, setting a threshold for binary representation, and subsequent filtering by keeping only the max. activity value for each compound/target pair. Retrieving GO annotations for a list of targets, and ChEBI annotations for compounds that have been tested buy ML213 against those targets was achieved by using the API calls `Target Classifications’ and `Compound Classifications’ and subsequent restriction to terms of the type `biological process’ and `has role’, respectively. doi:10.1371/journal.pone.0115460.g003 14 / 32 Open PHACTS.Advanced non-small-cell lung cancer, as well as pancreatic and breast cancer. However, current therapy treats only a subset of patients carrying specific mutations and even within this population, tumor resistance is common. Identification of specific protein targets involved in ErbB-mediated cancer development is confounded by the multiplicity of pathways activated by ErbB receptors and the existence of more than 100 potential protein binding partners identified by large-scale phosphoproteomic screening. As members of the ErbB receptor family cooperate in signal transduction and malignant transformation, the concurrent inhibition of two or more receptors or specific heteromeric ErbB family receptor complexes may yield the next generation targeted therapies. However, only a small proportion of publicly available bioactivity data reports on the activation of ErbB oligomers. In many cases, the exact mechanism of ligand-protein binding and protein activation is simply not known and bioactivity of small molecules is tested on single proteins only. This leads to challenges for structure-based drug design and interpretation of pharmacological data. As such, understanding the role of receptor oligomers in the ErbB signaling pathway is invaluable for the purpose of drug discovery. Pathway targets and pharmacology In total, 54 NCBI Gene IDs were retrieved as targets from the ErbB signaling pathway. Of those, only 35 single proteins returned pharmacological data with the applied bioactivity filters. Additionally, data for 12 protein families, 5 protein complexes, 2 protein-protein interactions and one chimeric protein containing a target from the pathway were retrieved, increasing the total number of targets to 55. While a pharmacology query without any filters would retrieve nearly 150,000 data points, filtering reduced the data to 108,014 bioactivities and 65,780 unique compounds. Using the pChEMBL values to filter bioactivities led to a significantly lower number of records as compared to -logActivity values: 53 targets, 65,817 bioactivity endpoints and 43,255 unique compounds. The pChEMBL filter restricts data to those that are equal to a specific value. Values that are reported to be `greater than’ or `less than’ will therefore be missing in the 13 / 32 Open PHACTS and Drug Discovery Research Fig.3. Use case B workflow. Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Compound pharmacology at the pathway level was retrieved by consecutive execution of the API calls `Pathway Information: Get targets’ PubMed ID:http://jpet.aspetjournals.org/content/119/3/418 and `Target Pharmacology: List’ – the latter includes a filtering for desired activity endpoints and units – and other filtering, transformation, and normalization steps: transformation into `- logActivity values ‘, setting a threshold for binary representation, and subsequent filtering by keeping only the max. activity value for each compound/target pair. Retrieving GO annotations for a list of targets, and ChEBI annotations for compounds that have been tested against those targets was achieved by using the API calls `Target Classifications’ and `Compound Classifications’ and subsequent restriction to terms of the type `biological process’ and `has role’, respectively. doi:10.1371/journal.pone.0115460.g003 14 / 32 Open PHACTS.